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Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such handcrafted features for strong performance, recent work (Wu et al., 2018) has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of "soft gazetteers" that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score. 1

While neural machine translation (NMT) achieves remarkable performance on clean, indomain text, performance is known to degrade drastically when facing text which is full of typos, grammatical errors and other varieties of noise. In this work, we propose a multitask learning algorithm for transformer-based MT systems that is more resilient to this noise. We describe our submission to the WMT 2019 Robustness shared task (Li et al., 2019) based on this method. Our model achieves a BLEU score of 32.8 on the shared task French to English dataset, which is 7.1 BLEU points higher than the baseline vanilla transformer trained with clean text 1 .

This paper studies the prescribed performance tracking control problem for a class of multi-input multi-output strict-feedback systems with asymmetric nonsmooth actuator characteristics and output constraints as well as unexpected external disturbances. By combining a novel speed transformation with barrier Lyapunov function, a neural adaptive control scheme is developed that is able to achieve given tracking precision within preassigned finite time at prespecified converging mode. At each of the first $n-1$ steps of backstepping design, we make use of the radial basis function neural networks to cope with the uncertainties arising from unknown and time-varying virtual control gains, and in the last step, we introduce a matrix factorization technique to remove the restrictive requirement on the unknown control gain matrix and its NN-approximation, simplifying control design. Furthermore, to reduce the number of parameters to be online updated, we introduce a virtual parameter to handle the lumped uncertainties, resulting in a control scheme with low complexity and inexpensive computations. The effectiveness of the proposed control strategy is validated by systematic stability analysis and numerical simulation.

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